The existing landmark localization algorithms for medical images cannot make good use of the inherent characteristics of medical images and cannot well perceive their subtle features. Therefore, this study proposes a landmark localization algorithm for medical images, which is based on topological constraints and feature augmentation. It uses the invariant topological structure among landmarks to improve the localization accuracy of the algorithm, and multi-resolution attention mechanisms and multi-branch dilated convolution modules are introduced into the network to extract augmented features. The network can not only pay more attention to important features but also improve the perception of context features without increasing the amount of computation and the number of parameters. Experiments on public datasets demonstrate that the proposed method outperforms the current mainstream algorithms in every indicator and achieves higher accuracy.